Smart carpet systems and methods of using same for monitoring physical and physiological activities

ABSTRACT

Systems and methods for using a smart carpet to monitor a health status of an individual. The smart carpet has electronics and a plurality of pressure sensing areas for sensing plantar pressure of the individual. A monitoring computer comprising a processor and a memory including machine readable instructions processes carpet data. A first part of the smart carpet is scanned at a first frequency and a second part of the smart carpet is scanned at a second frequency greater than the first frequency. Plantar pressures are received from the second part of the carpet and the instructions evaluate the plantar pressures to determine at least one of a physical activity and a physiological activity. Where an emergency condition is determined, an alarm is communicated to monitoring personnel.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/760,118 titled “Smart Carpet Systems And Methods Of Using Same ForMonitoring Physical And Physiological Activities”, filed Mar. 14, 2018,which is a national phase entry under 35 U.S.C. § 371 ofPCT/US2016/051969 titled “Smart Carpet Systems And Methods Of Using SameFor Monitoring Physical And Physiological Activities”, filed on Sep. 15,2016 which claims priority to U.S. Provisional Patent Application Ser.No. 62/218,919 titled “System And Methods For Automatically RecognizingAnd Tracking Physical And Physiological Activities For HealthcareMonitoring On A Smart Carpet”, filed Sep. 15, 2015. The contents of eachof the aforementioned patent applications are hereby incorporated byreference in their entirety.

FIELD OF THE INVENTION

This disclosure relates generally to monitoring body movement using asmart carpet. More specifically, the disclosure relates to monitoringsystems and methods that employ a smart carpet to automatically trackand evaluate health status and emergency conditions.

BACKGROUND

Falls and frailty are significant public health issues for successiveaging in place. Current technologies are often impractical fortele-monitoring of falls, risk of falling, and frailty, where monitoringover months or years is required.

SUMMARY

Systems and methods for monitoring health status of one or moreindividuals using a smart carpet are disclosed herein. According to anembodiment, a method of using a smart carpet to monitor a health statusof an individual is provided. The smart carpet comprises a plurality ofpressure sensors arranged as a two-dimensional array. Each pressuresensor includes a block of anti-static foam situated between at leasttwo perpendicularly oriented yarns. The method comprises operablycoupling to the smart carpet a monitoring computer having a processorand non-transitory memory. The non-transitory memory has computerimplemented instructions stored thereon. The method includes the step ofsimultaneously scanning a first part of the smart carpet at a firstfrequency and a second part of the smart carpet at a second frequencygreater than the first frequency, and the step of receiving carpet datafrom the smart carpet over a communication pathway. The carpet data isprocessed using the computer implemented instructions to determine eachof a physiological activity and a physical activity.

According to another embodiment, a smart carpet system for monitoring ahealth status of an individual includes a smart carpet havingelectronics and a plurality of pressure sensing areas for sensingplantar pressure. The system has a monitoring computer comprising aprocessor communicatively coupled to a memory. The memory includesmachine readable instructions which, when executed by the processor, arecapable of scanning a first part of the smart carpet at a firstfrequency and a second part of the smart carpet at a second frequencygreater than the first frequency. Plantar pressures are received fromthe second part of the carpet and the instructions evaluate the plantarpressures to determine at least one of a physical activity and aphysiological activity. Where an emergency condition is determined, analarm is communicated to monitoring personnel.

According to yet another embodiment, a smart carpet system formonitoring a health status of an individual comprises a smart carpethaving electronics and a plurality of pressure sensing areas for sensingplantar pressure of the individual. The system has anactivity-determining processor configured to implement machine readableinstructions to determine at least one of a physical activity and aphysiological activity, and an interface configured to communicateresults of the determination to remote monitoring personnel. The systemincludes an alarm generator configured to generate an alarm when theresults indicate an emergency event.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 schematically illustrates a portion of a smart carpet having agrid pressure sensor matrix made by conductive textile materials, in anembodiment.

FIG. 2 shows one exemplary smart carpet system for automaticallyrecognizing and tracking physical and physiological activities forhealthcare monitoring, in an embodiment.

FIG. 3 is a flowchart illustrating an example high level algorithm forscanning and tracking movement on the smart carpet of FIG. 1.

FIG. 4 is a flowchart illustrating an example high level algorithm,implemented using the system of FIG. 2, for recognizing and tracking thephysical and physiological activities on the smart carpet of FIG. 1.

FIG. 5 is a flowchart illustrating further exemplary detail of thealgorithm of FIG. 4 for identifying movements among one or more peopleas well as between people and objects, and for generating an emergencyalarm when a person suddenly falls on the carpet of FIG. 1.

FIG. 6 is a flowchart illustrating further exemplary detail of thealgorithm of FIG. 4 to evaluate clinical results for activity detectedon the carpet of FIG. 1 using the system of FIG. 2, in an embodiment.

FIG. 7 is a flowchart illustrating further exemplary detail of thealgorithm of FIG. 4 for measuring weight of a person on the carpet ofFIG. 1 using the system of FIG. 2.

FIG. 8 is a flowchart illustrating further exemplary detail of thealgorithm of FIG. 4 for calculating breathing and heart rates of aperson on the carpet of FIG. 1 using the system of FIG. 2.

FIG. 9 is a flowchart illustrating further exemplary detail of thealgorithm of FIG. 4 for generating an emergency alarm when a personsuddenly falls on the carpet of FIG. 1.

FIGS. 10A-10B show exemplary signal characteristics captured from thecarpet of FIG. 1 by the system of FIG. 2, in an embodiment.

FIG. 11 shows exemplary signal processing by the system of FIG. 2 todetermine breathing rate and heart rate from data of the smart carpet ofFIG. 1, in an embodiment.

DETAILED DESCRIPTION

Aging in place, i.e., the ability to continue to live safely in one'sown home irrespective of age, is becoming more important due to therapid increase in the population of senior citizens. The continuedpopulation growth of senior citizens may lead to significant financialand logistical burdens on the nation's healthcare system. As thepopulation ages, there will be a greater need for preventive, acute,rehabilitative, and long-term health care services for senior citizens.Consistent healthcare monitoring, which is particularly essential foraging in place senior citizens, will also become increasingly important.An objective method for unobtrusive healthcare monitoring, useable bynon-expert senior-citizens and in residential and community-caresettings, is desirable. The present disclosure, among other things,relates to a context-aware smart carpet system for healthcare monitoringthat is particularly useful for healthcare management of seniorcitizens.

A smart carpet system as a context-aware system for health statusmonitoring of senior citizens provides a practical form factor forremote and continuous screening of movements, and can be used to gathermeaningful clinical information such as frailty, risk of falling,outcomes of rehabilitation, plantar wound dressing conditions, emergencyconditions such as sudden falling due to heart attack, et cetera.Tomographic method-based and sensor array-based carpet systems in theprior art may allow for imaging and displaying position of footsteps.However, such systems are inadequate for health status monitoring asthey neither provide meaningful clinical information nor recognizeemergency conditions. Moreover, their exorbitant price prohibitsinstallation in home environments. There is a need for unobtrusivehealth status monitoring systems and methods that operate unsupervisedat any location (e.g. a person's home) to allow aging in place of seniorcitizens.

Indeed, aging in place is one of the key issues of modern societies. In2029, more than 20% of the total US population will be over the age of65, and by 2050, this population will have increased to 83.7 millionpeople, almost double the 2012 population estimate of 43.1 million.Continued population growth of these older adults (age 65+) will lead toa greater need for preventive, acute, rehabilitative, and long-termhealth care services, as well as a need for tools to enable them tofunction independently during everyday activities.

Falls and frailty are significant public health issues for successfulaging in place because they are the primary causes of injury and deathin older adults. Falls are the second leading cause ofaccidental/unintentional deaths. Approximately 424,000 people die fromfalls each year and 37.3 million falls are severe enough to requiremedical attention. In 2013, the total direct medical cost of fallinjuries for people over age 65 was $34 billion. And this costassociated with fall injuries is known to increase rapidly with age.Frailty is also a major health condition associated with aging and mayincrease the risk of falling, fear of falling, delirium, disability,hospitalization, long-term care, and even death. 25% to 50% of adultsover age 85 are estimated to be frail. Frailty exacts a high cost on anindividual in terms of quality of life, and the burden of frailty canaffect the ability of older adults to live independently. Currently,physicians typically rely on basic physical examinations to assessfrailty and risk of falling. As a result, only severe deficiencies arereliably identified. An objective method for assessing fall risk andfrailty that can be employed by non-experts in a community-care settingwould enable advanced screening for high risk individuals. However,there is currently no practical tool for everyday screening of fall riskand frailty during unsupervised conditions.

Remote monitoring systems have the potential to improve patient accessto healthcare. Nearly 20% of people in the United States live in ruralareas, but only 9% of physicians work in rural areas. Problems caused bylack of access to healthcare may exacerbate over time as manyorganizations predict there will be a shortfall in primary careproviders due to recent health care reform. Rural residents havesubstantially poorer healthcare than urban residents. When compared tourban residents, rural residents travel 2 to 3 times farther to see aphysician, see fewer specialists, and have worse health outcomes forcommon conditions such as diabetes and heart attack. Remote monitoringsystems can extend the reach of physicians to underserved rural areasand consequently reduce these health disparities.

Tele-monitoring of falls, fall risk and frailty can support successfulaging in place. Such monitoring may allow for evaluation of frailtyprogression and enables timely intervention after a fall, therebypromoting a more active lifestyle through a reduction in fear offalling. Monitoring daily activity patterns for fall risk and frailtywould enable more accurate and up-to-date assessments of physical healthas compared to infrequent physical exams. Such monitoring could alsoevaluate the efficacy of rehabilitation programs designed to reduce fallrisk.

Tele-activity monitoring is commonly used to quantify daily activity.Most tele-activity monitoring devices are wearable motion monitoringdevices such as a pedometer, actometer, accelerometer, and/or gyroscopeattached to the patient's wrist, waist, or ankle. These devices havebeen widely used in clinical studies to monitor circadian rhythms,hyperactivity, diabetic care, sleep disorders and Parkinson's disease.Although some devices have been developed to monitor risk of falls andfrailty, these devices have serious technological limitations such asshort battery life, vision obstruction/occlusion problems withcamera-based systems, and user resistance to wearing wearable sensors atall times.

A smart carpet as disclosed herein may be used for tele-activitymonitoring and may track gradual changes in gait and motor performance.The smart carpet may also recognize emergency situations such as a fallor the inability to rise from the carpet. The carpet is a non-wearablesystem for tele-activity monitoring that may enable continuous, remotescreening of fall risk and frailty based on a patient's motorperformance and activities. If desirable, the carpet may also becombined with other means of measurement including wearable technologyand camera based motion tracking systems.

The disclosed smart carpet may reduce fear of falling and promote activeliving in older adults, and as such, decrease the need for in-clinicmonitoring. The smart carpet may be constructed from smart textiles,which have been successfully used for long-term monitoring ofphysiological parameters in a variety of healthcare domains such astele-monitoring, rehabilitation, ergonomics, and sports medicine.Furthermore, smart textiles in the form of clothing and bedding havebeen used to monitor neurological and cardiovascular disorders anddetect human motion. However, a smart textile-based carpet, practicalfor residential, nursing home, and acute settings, has not yet beendeveloped to monitor, analyze, and assess fall risk and frailty.

The disclosed smart carpet may have a positive impact on the ability ofolder adults to age in place. The smart carpet may provide earlydetection of fall risk and frailty in older adults, which may result infewer falls in this population. The smart carpet may be particularlybeneficial in emergency situations (e.g., a fall during unaccompaniedmovement). Such reduction in the number of falls will help alleviatehealthcare costs in older adults and improve quality of life by allowingsenior citizens to remain in their homes. Continuous in-hometele-monitoring, as discussed herein, may also reduce the possibility ofinjury or death because it may request immediate assistance when a fallor other emergency condition is detected. The smart carpet may providepeace of mind to older adults fearful of injury and may enable them tofeel comfortable aging in place, and consequently, improve their qualityof life. The smart carpet may also monitor disease progression fordiseases such as diabetes, stroke, and Parkinson's that affect mobilityand balance. The smart carpet may conduct non-intrusive continuous dailyactivity monitoring of fall risk and frailty, and may advance the stateof the art in fall risk assessment and continuous home-based healthmonitoring for successful aging in place.

The smart carpet may have several advantages over conventional physicalactivity trackers. One of the issues in fall risk assessment andactivity tracking is the necessity of continual monitoring over extendedperiods of time (e.g., months or years). Many elderly people stopwearing sensors or carrying cellphones with monitoring applications overthese long time periods. As such, a non-wearable monitoring device likethe smart carpet has substantial advantages because it operatescontinuously without changing the user's normal routine and providesdetailed data about gait and motor performance during locomotion,sitting, and standing. Research-grade smart rugs (e.g. GaitRite™Walkway) that map the temporal pressure profiles of footsteps are usedby research clinics, but they are not suitable for residential settings.For instance, they cannot be used on stairs due to their lack offlexibility, and their high cost renders them unsuitable forwall-to-wall arrangement in homes. And, to effectively detect emergencyevents and track deterioration in motor performance, such wall-to-walland up-the-stairs monitoring in homes is a requirement.

The disclosed smart carpet uses highly flexible and washable conductiveyarns as the sensing elements, which may be either attached to theunderside of regular residential carpet or integrated directly into thecarpet's fibers. Other flexible sensing units like hybrid flexiblesensors may be also used to form the smart carpet. The smart carpet maybe extremely flexible and may be easily folded and/or rolled up forstorage or movement. The sensing elements may be washable, which mayimprove longevity. Moreover, the smart carpet may be economical becausethe conductive yarns and/or flexible sensing elements used in the carpetare inexpensive.

Prior art floor based sensing systems (e.g. SenFloor™) based ontomographic methods identify footstep and display position and footfallof a person walking on the floor. However, the use of fiber optics forsensing is often unsuitable for residential applications due to theirhigh fragility and cost. Such systems also do not incorporateintelligent methods to extract clinically meaningful information relatedto risk of falling, frailty, and identification of emergency situations.

The disclosed smart carpet system may be used also to collect healthstatus information, such as respiration rate, heart rate, and heart ratevariability, by assessing fluctuation in plantar pressure of a person,as well as in-chair pressure during sedentary behavior. The smart carpetsystem may use this information to identify deterioration in healthstatus or emergency events such as heart attack and/or stroke. The smartcarpet system may also be used for tracking outcomes of an intervention,e.g., by monitoring changes in motor performance in Parkinson's as afunction of medication or deep electrical stimulation, and may be usedto track changes in pain status by determining the quality of motorperformance, such as duration of rising from a chair, gait initiation,gait speed, et cetera.

The application of the disclosed smart carpet system is not limited tosenior citizens or residential settings. By measuring heart rate, heartrate variability, respiration rate, et cetera, the smart carpet systemmay also unobtrusively track stress in office workers, and theinformation gleaned may be used for modifying environmental stressors toenhance well-being and health of office workers. The smart carpet systemmay also unobtrusively track mental stress including anxiety,depression, sleep disorders, etc., and may track unobtrusivelycardiopulmonary activity related to sleep quality for monitoring andsleep disorders including sleep apnea, insomnia, and somnambulism.

The smart carpet system may also include other sensor modalities and/ormay be combined with other types of sensors to track environmentalinformation such as noise, humidity, air quality, light quality,physical activity, et cetera, to better track heath status andenvironmental factors which may impact health.

The smart carpet system may also track adherence to prescribed footwearby identifying and comparing foot print patterns to expected foot printpatterns of the prescribed foot wear. This may have application forprevention of diabetic foot ulcers and/or management of wound healing inpatients with diabetics.

The smart carpet system may also include an auditory sensor foridentifying talking while walking and thereby operate to measure theability of a person to walk while talking. The smart carpet may alsoassess motor performance while talking as compared to motor performancewithout talking, which information may be used to evaluate the cognitivestatus of the user.

Focus is directed now to FIG. 1, which schematically illustrates aportion of a smart carpet 100 having a grid pressure sensor matrix madeby conductive textile materials. Each textile-based pressure sensor maybe made with a square shaped section of anti-static foam 102 situatedbetween two perpendicularly oriented conductive yarns 104, 106. Theresistance value of each of the anti-static foam 102 and the conductiveyarns 104, 106 may be inversely proportional to the pressure applied tothe foam 102. When no pressure is applied to the anti-static foam 102,the resistance value of the foam 102 may be very high, and no currentmay be conducted therethrough. Electric current may flow through thefoam 102 when pressure is applied to the foam 102.

The conductive yarns 104, 106 connecting each pressure sensor may behighly flexible. This characteristic may allow the smart carpet 100 tobe easily spread out, packed up, and moved. Such flexibility may alsoallow the smart carpet 100 to be used in different residential settings(e.g., on stairs).

FIG. 2 schematically shows one exemplary smart carpet system 200 forautomatically recognizing and tracking physical and physiologicalactivities for health status monitoring. Smart carpet system 200includes smart carpet 100 of FIG. 1 and associated circuitry 202communicatively coupled to a monitoring computer 204.

Smart carpet 100 may consist of an m×n pressure sensor matrix with pixelresolution of 1 cm². The associated circuitry 202 may include one ormore of: multiplexers 206, demultiplexers 208, high impedance buffer210, analog-to-digital converter (ADC) 212, highly resistant resistors214, and a microcontroller unit 216 (or another controller). In someexample embodiments, the microcontroller unit 216 be configured throughparticularly configured hardware, such as an application specificintegrated circuit (ASIC), field-programmable gate array (FPGA), etcetera, and/or through execution of software to perform functions inaccordance with the disclosure herein.

Each pressure sensor may be located at the intersectional node of twoperpendicular yarns (e.g., yarns 104, 106 in FIG. 1). DC voltage inputmay be applied on vertical yarns (referenced as yarns 218 in FIG. 2) andanalog output voltage may obtained on the horizontal yarns (referencedas yarns 220 in FIG. 2). To identify the pressed sensors, the whole gridmatrix may be scanned one node by one node. At each instant, the DCinput voltage may only be applied to one vertical yarn, and only oneanalog output may read from one horizontal yarn. If the correspondingsensor, which is located at the intersectional node of the two selectedperpendicular yarns, is pressed, a non-zero analog voltage may be readat the output channel 221 as an input to the ADC 212. If thecorresponding sensor is not pressed, the output reading may be zero.

The multiplexers 206, demultiplexers 208, and microcontroller unit 216may be used to effectuate the sensor matrix scanning. Demultiplexers 208may be used at input channels for the selection of vertical yarns 218,and multiplexers 206 may be used at output channels for the selection ofhorizontal yarns 220. When the p-th horizontal yarn and the q-thvertical yarn are selected, the analog output may identify whether the(p, q)-th sensor is pressed or not.

The select signals of multiplexers 206 and demultiplexers 208 may beprovided by the microcontroller unit 216. The microcontroller unit 216may further be programmed to control the select signals in a desiredfrequency. At each clock cycle in the microcontroller unit 216, only oneof the m horizontal yarns (i.e., yarns 220) and one of the n verticalyarns (i.e., yarns 218) are selected. If the sensor at the correspondingintersectional node is pressed, the resistance of the anti-static foambecomes low; electric current thus flows from the vertical yarn to thehorizontal yarn, and a voltage output appears at the analog outputchannel 221. Since the resistance of the conductive yarns and theanti-static foam is pressure sensitive, the output voltage variesaccording to the applied pressure. After the analog-digital conversionat ADC 212, different analog voltages are transformed into correspondingdigital levels indicative of the pressure at digital output 222, and thedigital output 222 may be communicated to the monitoring computer 204.Smart carpet 100 measures relative pressure values (e.g., weight) ofobjects that are stationary or moving on its surface. System 200, viathe monitoring computer 204, continuously monitors activity patterns ofdaily living and also automatically recognizes falling and otheremergency situations to manage the healthcare of one or more people.System 200 may also be used to manage adherence to offloading inpatients with diabetes, and other similar therapies.

The monitoring computer 204 may have at least one processor 226 andmemory 228. The processor 226 represents one or more digital processors,and the memory 228 represents one or more of volatile memory (e.g., RAM)and non-volatile memory (e.g., ROM, Flash, magnetic media, opticalmedia, et cetera). Software 230 may be stored in a non-transitoryportion of the memory 228. The software 230 includes machine readableinstructions that are executed by processor 226 to provide thefunctionality of the monitoring computer 204, as described herein.

In an example embodiment, software 230 includes a plurality ofalgorithms that operate to automatically recognize, track and detectactivities such as one or more of daily living, a falling event, andfrailty of individuals, to provide healthcare monitoring in places suchas one or more of a residence, office, hospital, et cetera. For example,the software 230 may include a recognition algorithm 232, a trackingalgorithm 234, an activity algorithm 236 for determining and monitoringactivity, a fall algorithm 238 to determine a fall condition, et cetera.The processor 226 may receive digital data from the output 222 andprocess same to monitor the health of one or more individuals on thecarpet 100. Monitoring computer 204 may also include a remote interface240 for communicating with other computer devices, and an alarm actuator242 for raising an alert when certain situations are detected on smartcarpet 100. Monitoring computer 204 may in embodiments be combined withsmart carpet 100 (e.g., at least some of the functionality thereof maybe implemented using the microcontroller unit 216) without departingfrom the scope hereof.

The smart carpet digital output 222 may be communicated from the carpet100 to the monitoring computer 204 over a pathway 224. The pathway 224may be wired, wireless, or a combination of wired and wireless pathways.In an embodiment, a monitor (e.g., display 246 of monitoring computer204) may be used to display the pressed sensor nodes. The digital outputat the digital output 222 may be represented on the display 246 in sucha way that the viewer can readily determine which nodes are beingpressed and how much pressure is being applied each node relative toother nodes. For example, the monitoring computer 204 may be configuredto present on the display 246 a color-coded map representing thepressure levels (e.g., the depressed foam squares may appear as red,blue, green, and yellow on the display 246 to indicate differingpressure levels). If the sensor at the corresponding intersectional nodeis not pressed, the resistance of the foam stays high, the foam isconsidered as “nonconductive” and both analog and digital outputs arezero.

System 200 is designed to be unobtrusive and nonintrusive when operatingin any environment, and may include intelligent algorithms (e.g.,algorithms 232, 234, 236, and 238) discussed in more detail below thatcooperate to: (1) automatically distinguish between one or moreindividuals and objects by analyzing movements of these individual(s)and object(s) on the smart carpet 100; (2) classify activity patterns(e.g., classify that an individual on the smart carpet 100 is standingup from a sitting position, is sitting down from a standing position, iswalking, is running, et cetera); (3) provide clinically meaningfulinformation such as risk of frailty and risk of falling in elderly byquantifying key daily motor performances (e.g., number of times anindividual sat during a predefined period, the speed at which theindividual sat down, the quality of his turns, spatial temporalparameters of gait, gait initiation, gait variability, ability ofwalking while talking, et cetera, that are indicators of risk of fallingand frailty status); (4) recognize effects of plantar wound dressing inpatients with diabetes and their adherence to prescribed footwear; (5)automatically generate an emergency alarm signal to indicate an alarmcondition (e.g., a falling event); and (6) provide output that may becombined with information from other fabrics, sensors (RF, Bluetooth,etc.), smart phones, and/or watches for data logging, transmitting andmonitoring.

System 200 may operate in a real-time environment and under unsupervisedconditions. Specifically, unlike other monitoring solutions that utilizesensors such as accelerometers, gyroscopes, and force sensitiveresistors to identify movement of individuals, electrodes to acquire anelectrocardiogram for evaluation of heart rate variability, and so on,system 200 may not require any additional sensors to capture similarinformation.

FIG. 3 is a flowchart illustrating a high level algorithm 300implemented by smart carpet system 200 of FIG. 2 for scanning andtracking movement on smart carpet 100 of FIG. 1 (using, e.g., therecognition algorithm 232 and the tracking algorithm 234). The algorithm300 may have two high level scenarios: a scanning scenario 302 and atracking scenario 306.

The system 200, via the monitoring computer 204, may employ scanningscenario 302 of the algorithm 300 to detect motion of one or moreindividuals. When in scanning scenario 302, smart carpet system 200 mayoperate with a low spatial resolution (for example 10 cm) and lowsampling frequency (for example 1 Hz). In this example case, one in tenof the vertical yarns 218 and one in ten of the horizontal yarns 220 arescanned and the whole sensor matrix is scanned only once per second.Based on the result of the scan, the system 200 may detect human motionby using movement algorithms (described below). If no human motion isdetected at step 303, smart carpet system 200 may continue to operateunder only the scanning scenario 302 with low spatial resolution and lowsampling frequency.

If human motion is detected on the smart carpet 100 at step 303, at step304 the algorithm 300 may identify the location of the individual who ismoving on the smart carpet 100 and demarcate a certain region (e.g., a100 cm×100 cm region or another square region) with the human positionas the midpoint. Then, tracking scenario 306 may be applied to thedemarcated region(s) with high spatial resolution (for example 1 cm, soevery vertical and horizontal yarn 218, 220 passing through the squareregion is scanned) and high sampling frequency (for example 40 Hz, sothe whole sensor matrix in the square region is scanned forty times persecond).

The high spatial resolution and high sampling frequency for the trackingscenario 306 may ensure accurate and meticulous human movementmonitoring by system 200. The marked out square region location may beupdated in real time with the human movement (i.e., the demarcated areabeing sampled at high frequency and high spatial resolution may beadaptively modified such that its midpoint generally corresponds to thecurrent location of the individual). In an embodiment, while part ofsmart carpet system 200 is working under the tracking scenario 306,large areas of the carpet 100 may still be evaluated using the scanningscenario 302 because no motion has been detected in or immediatelyproximate these areas. The artisan will understand that the trackingscenario 306 may be more computationally intensive relative to thescanning scenario 302. By limiting the tracking scenario 306 to thoseregions of the carpet 100 on which movement is detected, costsassociated with the system 200 may be reduced.

FIG. 4 is a flowchart illustrating a high level algorithm 400,implemented in system 200 of FIG. 2, for recognizing and tracking thephysical and physiological activities on smart carpet 100 of FIG. 1.Algorithm 400 may be implemented at least in part within software 230(e.g., some or more of the algorithm 400 may be implemented via theactivity algorithm 236). Additional detail regarding the functionalityof the algorithm 400 is provided in FIGS. 5-11 and the associateddiscussion below.

The algorithm 400 may begin at step 402, where the monitoring computer204 may acquire raw data from textile-based sensors of the smart carpet100 (e.g., via communication pathway 224 (FIG. 2)). As discussed in moredetail herein, the amplitude of the raw data may depend on the weight ofthe human(s) or object(s) on the smart carpet 100.

The acquired raw data may then be preprocessed using signal processingtechniques at step 404 so that high quality measurements (e.g., movementclassifications, measurements of breathing rates, heart rates, bodyweight, et cetera) may subsequently be made. The preprocessingtechniques at step 404 may include wavelet transform, filtering (e.g.,digital low pass, high pass, or adaptive filtering), averaging,sampling, et cetera. The system 200 may use different optimal bandwidthsfor different purposes (e.g., different bandwidths may be used toclassify movement, measurement of breathing rates, measurement of heartrates, measurement of body weight, et cetera).

The preprocessed data may be used to determine movements at step 406.The movements of step 406 may be categorized in the system 200 as staticmovements 414 or dynamic movements 416. The static movement 414categorizations may include standing 418, stand-to-sit 420, sitting 422,sit-to-stand 424, and lying 426; FIG. 10, discussed below, shows oneexample of how the different static movements 414 may be identified. Thedynamic movement 416 categorizations may include walking 428, running430, and falling 432. FIG. 5, discussed below, describes detailedmethods for classification of various static movements 414 and dynamicmovements 416.

The system 200, via the algorithm 400, may also calculate breathing rate408 and heart rate 410, as discussed in more detail below with referenceto FIG. 11. The system 200 may further be used to compute body weight412 of a person on the carpet 100. As shown in FIG. 4, the breathingrate 408 may be measured at step 408A during sitting 422, at step 408Bduring lying 426, and during or immediately after a fall 432, using dataacquired by the smart carpet 100. The breathing rate 408 may not becalculated for other movements (e.g., standing 418, stand-to-sit 420, etcetera). In an embodiment, the breathing rate 408 may be calculatedwhile sitting 422, lying 426, and falling 432 over five seconds (oranother unit of time) when the data indicates that the person on thecarpet 100 is relatively stationary.

The heart rate 410 may likewise be computed during certain movements ofstep 406, but not at others. For example, as shown in FIG. 4, the heartrate 410 may be measured at steps 410A, 410B, and 410C only when themovements of step 406 are respectively categorized as sitting 422, lying426, falling 432. Akin to the breathing rate 408, the heart rate 410 maybe measured over five seconds (or another unit of time) when the datafrom the carpet 100 indicates that the person on the carpet 100 isrelatively stationary. Body weight 412 may be measured only duringstanding 418 at step 412A.

Once the movements of step 406 have been categorized as static movements414 or dynamic movements 416, the system 200 may use the variousmovement data to analyze the daily physical activities at step 433. Atstep 434, the system 200 may use the daily physical activity analysisperformed at step 433 (or the movement data) to identify and evaluatespatial and temporal gait parameters 434. The spatial and temporal gaitparameters 434 evaluated by the system 200 may include, for example,walking speed 434A, cadence 434B, step length 434C, step frequency 434D,walking pattern 434E, et cetera. The system 200 may also use thebreathing rate 408 and heart rate 410 to monitor an individual'sphysiological activities at step 436.

The system 200 may be capable of generating an emergency alarm at step438 in real-time if the temporal and spatial gait parameters 434 and/orthe physiological activities monitoring 436 indicates an emergency orabnormal condition. For example, an alarm may be generated (e.g., asiren may go off) if the data indicates that the individual beingmonitored has fallen. The alarm 438 may also be communicated to remotemonitoring personnel in one or more ways (e.g., the system 200 may causea phone call to be placed to paramedics and/or may page a physician). Insome embodiments, to reduce the risk of false alarms, the alarm 438 maybe generated when at least two conditions are met. For example, thealarm 438 may be generated when the individual being monitored falls andstays down (e.g., lying 426) for ten seconds (or another unit of time);or, for example, the alarm 438 may be generated where the heart rate ofthe monitored individual appears abnormal in two (or more) successivereadings.

At step 440, the clinical results may be evaluated. Specifically, thespatial and temporal gait parameters 434 and the physiologicalactivities monitoring 436 results may be evaluated to determine clinicalresults (e.g. risk of frailty, effect of plantar wound dressing, etcetera).

The results (and/or the raw data) may be communicated by the system 200to a server or mobile device at step 442 (e.g., the server associatedwith a senior care facility or hospital and/or the mobile device of afamily member). This transmission may be in real-time, or data may bestored and transmitted on a period basis (e.g., once every day).

FIG. 5 is a flowchart illustrating algorithm 500 for categorizingmovement of individuals as discussed above with respect to FIG. 4. Thealgorithm 500 may be used to classify the movement of an individual, todistinguish between the movement of two or more individuals or objects,and to generate an alarm as discussed above for FIG. 4. The algorithm500 may be implemented at least in part by processor 226 and software230.

At step 502, the monitoring computer 204 (FIG. 2) may acquire raw datafrom the smart carpet 100. At step 504, the raw data may be preprocessed(e.g., filtered, averaged, sampled, et cetera) as discussed above forstep 404 of algorithm 400. At step 506, movement may be detected andclassified.

The movement classification step 506 may involve extraction of variousfeatures from the preprocessed data at step 508. The feature extractionstep 508 may include signal processing techniques such as time-domainprocessing (mean, standard deviation, skewness, kurtosis, velocity,acceleration, jerk, etc.), frequency-domain processing (fast Fourieranalysis, power spectrum analysis, etc.), time-frequency coefficientanalysis, wavelet coefficients, etc. Among the recognized features, theoptimized features may be clustered for selection.

The clustering step 510 may involve grouping of optimized and selectedfeatures. The clustering step 510 reveals whether the data in somepositions is due to human movement or object movement. For example,where the data indicates that the area on the smart carpet 100 on whichpressure is applied is in the shape of human feet, the system 200 maydetermine that the movement is that of an individual. Alternately, wherethe area on the smart carpet 100 on which pressure is applied is in theshape of an object (e.g., wheels of a wheelchair), the system 200 maydetermine that the detected movement is object movement. The clusteringstep 510 may further show the number of people on the carpet (e.g., ifthere are two separate areas on which pressure is applied and each ofthem is in the shape of human feet, the system 200 may recognize thattwo individuals are standing on the carpet 100).

Based on the clustering step 510, the various static and dynamicmovements of humans and objects may be classified at step 512. Theclassification step 512 may involve implementation of patternrecognition techniques, such as artificial neural networks, supportvector machines, Bayesian classification, hidden Markov model, etc. Theremaining steps 514-530 of the algorithm 500 may be implemented as partof the activity classification step 512.

More specifically, the system 200, via the software 230, may identifystatic movements 514 where there is little signal variation or where thesignal variation is irregular. Where little signal variation isdetermined at step 516, the system 200 may characterize the movement asthat of an object at step 518, as people cannot stand withoutappreciable motion (because of heart activity, breathing, et cetera).

Where irregular signal variation is determined at step 521, the system200 may classify the static movement as human activity at step 522.Static human activities may include standing 522A, stand-to-sit 522B,sitting 522C, sit-to-stand 522D, and lying 522E (i.e., activitiesperformed while the monitored individual is generally confined to thesame area of the carpet 100).

The static human activity 522 may be characterized as standing 522Awhere the clustered area resembles the shape of feet and the signalamplitude is relatively large (i.e., the plantar pressure being appliedto the smart carpet 100 is relatively high because of the weight of thehuman). If the signal amplitude successively decreases from a standingcondition, the system 200 may determine a stand-to-sit activity 522B, asalso discussed with respect to FIG. 10 below. The system 200 mayidentify the human activity 522 as sitting 522C where the clustered areashape resembles that of the monitored individual's feet (or partthereof), and where the signal amplitude is relatively faint (i.e., thefull weight of the human is not being carried by the feet). The system200 may determine sit-to-stand activity 522D where from the sittingactivity the signal amplitude successively increases (indicating thatthe weight of the human is being borne by the feet). Where the clusteredarea is large (e.g., over five feet long), the system 200 may determinea lying activity 522E. The system 200 may be programmed to havedifferent thresholds to allow for monitoring individuals havingdiffering weights and sizes.

The system 200 via the algorithm 500 may classify dynamic movement atstep 520. Dynamic movement 520 may be identified if one or more objectsor humans traverse an appreciable area (e.g., one foot, two feet, etcetera) within a given time period (e.g., within five seconds, withinthree second, and so on).

If the dynamic movement 520 is continuous movement as determined at step523, the system 200 may classify the movement as the movement of anobject at step 525. Movement of an object may be classified at step 525as object movement where the clustered area moves continuously andsmoothly on the carpet 100 (e.g., indicating the movement of the wheelsof a wheelchair).

If, on the other hand, the dynamic movement 520 is discontinuous asidentified at step 524, the system 200 may at step 526 categorize thedynamic movement as human activity. For example, if the clustered areachanges appreciably with time (e.g., changes from big in a first area tosmall then to big again in a second area proximate the first area torepresent the pressure applied by the feet while the individual ismoving), the system at step 526 may categorize the dynamic activity ashuman activity.

Human activity 526 may further be categorized as one of sudden motion atstep 528 or normal activities at step 530. Sudden motion 528 may be forexample falling 528A or another sudden motion. The system 200 mayidentify falling at step 528A where the signal amplitude suddenlyincreases abnormally. In some embodiments, to identify falling, thesystem 200 may also determine whether the area on the carpet 100 onwhich pressure is applied is relatively large.

Where the variation of the signal amplitude is within a given threshold(i.e., is low), the system 200 may identify the normal activity 530 aswalking at step 530A or running at step 530B. If the signal amplitudechanges slowly, the system 200 may identify the normal activity 530 aswalking at step 530A. Where the signal amplitude changes quickly, thesystem 200 may identify the normal activity 530 as running 530B. Asbefore, in identifying these normal activities 530, the system 200 mayalso confirm that the area on the carpet 100 on which pressure is beingapplied indicates the presence of human feet.

FIG. 6 is a flowchart illustrating algorithm 600, and provides furtherexemplary detail of algorithm 400 of FIG. 4 to evaluate clinical resultsfor activity detected by carpet 100 of FIG. 1 using system 200 of FIG.2. The clinical results include the risk of frailty in elderly and theeffect of plantar wound dressing in patients with diabetic foot ulcers.

At step 602, the system 200 (e.g., monitoring computer 204), via thealgorithm 600, may acquire raw data from textile-based sensors of thesmart carpet 100 (e.g., over communication pathway 224). At step 604,the system 200 may adaptively filter the data using different cutofffrequencies to identify different movements, as discussed above forFIGS. 4 and 5. More specifically, the system 200 may determine movementat step 606, as set forth above for step 506 of the algorithm 500. Atstep 608, as discussed above for step 512 and associated steps ofalgorithm 500, the system 200 may classify the movement activity (e.g.,as static movement and dynamic movement). At step 610, after themovement activities have been classified, the system 200 may evaluateactivity patterns (e.g., determine that the monitored individual(s) arenot lying down during the day for an extended period of time, have notfallen, et cetera). The criteria used for such evaluation may bedifferent for different individuals (e.g., activity patterns may beevaluated differently for a mobile 60 year old individual and arelatively sedentary 95 year old individual).

At step 612, the system 200 may assess if the monitored individual has abalance problem. The system 200 may determine a balance problem at step612 if it senses asymmetry in standing balance, abnormal waking speed,an irregular walking pattern, et cetera.

In addition to classifying movements, the system 200, via the algorithm600, may monitor heart rates at step 614. Specifically, the system 200may measure heart rates at step 616 after the movement activities havebeen classified at step 608. Such may allow the system 200 to take intoaccount the movement activity in the heart rate measurements. Forexample, the system 200 may determine that a relatively high heart rateis not a cause for alarm where the monitored individual is walking orrunning, et cetera.

The system 200 may evaluate the variability of the heart rate at step618. Testing for such heart rate variability may allow the system 200 torecognize, for example, cardiac autonomic control problems.

At step 622, the system 200 may merge the gleaned movement informationwith the physiological assessments to obtain clinically viableinformation about the monitored individual. For example, at step 624,the system 200 may use the clinical results to evaluate the risk offrailty of the monitored individual. Or, for example, at step 626, thesystem 200 may evaluate the effect on an individual of plantar wounddressing (e.g., compare actual foot print patterns to expected footprint patterns).

FIG. 7 is a flowchart illustrating a method 700, and provides furtherexemplary detail of algorithm 400 of FIG. 4 for measuring weight oncarpet 100 of FIG. 1 using the system 200 of FIG. 2.

At step 702, the monitoring computer 204 may acquire raw data fromtextile-based sensors of the smart carpet 100 (e.g., via communicationpathway 224). At step 704, the system 200 may ascertain that themonitored individual is standing. For example, the system 200 maydetermine that the monitored individual is standing where the clusteredarea resembles the shape of human feet and the signal amplitude isrelatively large.

At step 706, the system 200 may measure the body weight of the monitoredindividual. Body weight measurement at step 706 may include sub-steps706A, 706B, and 706C. At step 706 a, the system 200 may wait until itdetermines that the monitored individual has been standing for over fiveseconds (e.g., that there has been no large movement on the carpet 100for five seconds or another time unit) so as to obtain a reliablereading. At step 706B, the system 200 may calculate the plantar pressurebeing applied to the carpet 100 by the monitored individual (or group ofindividuals). At step 706C, the system 200 may estimate the body weightof the monitored individual(s) using the calculated plantar pressure.The artisan will understand that the plantar pressure may correlate tothe body weight of the individual. In an embodiment, in estimating thebody weight, the system 200 may also take into account the size of thearea of the carpet 100 on which pressure is being applied.

FIG. 8 is a flowchart illustrating a method 800, and provides furtherexemplary detail of algorithm 400 of FIG. 4 for calculating breathingand heart rates on carpet 100 of FIG. 1 using system 200 of FIG. 2.

At step 802, the monitoring computer 204 may acquire raw data fromtextile-based sensors of the smart carpet 100. At steps 804 and 806, thesystem 200 may determine whether the monitored individual is standing orlying, respectively. If the monitored individual has been standing forover five second (or a different time unit, e.g., ten seconds) withoutappreciable movement at step 808, at step 812, the system 200 maydetermine the minute (i.e., fine) movements of plantar pressure, asshown in the plantar pressure raw signal in FIG. 11 for example.Similarly, if the monitored individual has been lying for five seconds(or a different time unit) at step 810, the system 200 may determine theminute movements of plantar pressure at step 812.

At step 814, the system 200 may detect the quasi-periodic shape of thefiltered signal so as to extract breathing and heart rates at steps 816and 818, respectively, as shown in more detail in FIG. 11. At step 820,the system 200 may use the determined breathing and heart rates tomonitor physiological activities of the monitored individual or group ofindividuals. If an abnormal or emergency condition is detected,monitoring personnel may be apprised of same in real time (e.g., viaremote interface 240 and/or alarm actuator 242).

FIG. 9 is a flowchart illustrating algorithm 900, and provides furtherexemplary detail of algorithm 400 of FIG. 4 for generating an emergencyalarm when somebody has fallen suddenly on carpet 100 of FIG. 1 usingsystem 200 of FIG. 2.

At step 902, the system 200 may, where applicable, detect falling asdiscussed above (e.g., for step 528A of algorithm 500). If the monitoredindividual is determined to have fallen, or where he is simply lying asdetected at step 908, the system 200 may calculate the time of lyingmovement at step 910. At step 912, the system 200 may determine whetherthe lying time as detected at step 910 is abnormal (e.g., is too long,such as over ten seconds). If so, the system 200 may generate anemergency alarm at step 914 to apprise monitoring personnel (e.g.,remote monitoring personnel) of the emergency condition.

The system 200 may also concurrently evaluate the physiologicalconditions of the monitored individual(s). Specifically, at step 916,after the detection of falling at step 904 and/or after the detecting oflying at step 908, the system 200 may measure the heart rate andbreathing rates at steps 916 and 918. At step 920, the system 200 maydetermine whether the heart rate or the breathing rate is abnormal forlying posture. If so, the system 200 may generate the emergency alarm(e.g., as discussed above for step 438 of algorithm 400). In someembodiments, the system 200 may also take into account an abnormal lyingand/or falling time (as determined at step 912) in determining whetherthe heart rate and/or breathing rate is not as expected.

FIG. 10 shows exemplary signal characteristics captured from carpet 100of FIG. 1 by system 200 of FIG. 2. More specifically, FIG. 10A shows amap 1000 detailing the location of the clusters of the carpet 100 onwhich pressure is being applied (using, e.g., the tracking scenario 306of FIG. 3), and the relative pressure being applied to constituents(e.g., square blocks) of these clusters. FIG. 10B shows a graphicalillustration 1050 that corresponds to the map 1000.

The map 1000 and the corresponding graphic illustration 1050, as shownin FIGS. 10A-10B, detail how various characteristics of a monitoredindividual may be determined in an example embodiment. In the FIGS.10A-10B example, the characteristics shown in map 1000 and graph 1050include: A) standing movements, B) stand-to-sit movement, C) sittingmovement; and D) sit-to-stand movement.

In an embodiment, the map 1000 may be displayed on the display (e.g.,display 246 of monitoring computer 204) as a color-coded map that showsthe relative pressure being applied to different areas of the carpet100. For example, the square blocks of the carpet 100 may be representedas red blocks, green blocks, blue blocks, yellow blocks, et cetera, toillustrate the carpet areas on which successively lower pressure isbeing applied. In this way, the map 1000 may denote not only thepertinent areas of the larger carpet 100 on which pressure is beingapplied, but also the relative pressure being applied to the sensors inthese areas. In the FIG. 10 example, the map 1000 includes boxes 1002and 1004A, 1004B. The box 1002 represents the plantar pressure beingapplied to the carpet 100 by the monitored individual, whereas boxes1004A and 1004B respectively show left and right wheels of a chair(e.g., a wheelchair).

The graphical illustration 1050 shows the plantar pressure signalgraphed against time. More specifically, the graph 1050 has pressure(e.g., in atmospheric units or another unit) on the y-axis and time(e.g., in seconds) on the x-axis. The signal 1052 shows the pressurebeing applied to the carpet 100 at various times by the feet of themonitored individual, which are also shown in map 1000. The signal 1054shows the pressure being applied to the carpet 100 at various times bythe wheels of the wheelchair (or another chair), as also shown in map1000. The graph 1050 also demarcates standing condition 1056 and sittingcondition 1058, respectively.

Turning now to the map 1000, standing A may be identified where the areaon the carpet 100 on which pressure is applied is in the shape of feet(see box 1002), and the applied pressure is relatively high. Map 1000likewise shows that the pressure being applied by the wheelchair wheels(as indicated in boxes 1004A, 1004B) is low in comparison, which wouldbe expected when the monitored individual is in a standing condition A.

Graph 1050 illustrates these concepts further. As can be seen, when themonitored individual is standing (i.e., at the time corresponding to “A”in graph 1050), the pressure exerted by the wheels of the wheelchair islow, whereas the plantar pressure is high in comparison.

Stand-to-sit activity B may be identified by the system 200 where theplantar pressure representation (i.e., box 1002) shows that the pressurebeing applied is less than the pressure applied at standing activity A.As the person is sitting down on the wheelchair (or another chair), thepressure being applied by the wheels (or legs) of the chair is higher ascompared to standing activity A. This is also illustrated in graph 1050.When the monitored individual is sitting down from a standing position(i.e., at the time corresponding to “B” in graph 1050), the pressure ofthe wheels is increasing rapidly and the pressure being applied by thefeet is quickly decreasing.

Sitting C may be identified where the map 1000 indicates that thepressure being applied by the wheelchair wheels (i.e., boxes 1004A,1004B) is significant compared to the plantar pressure (shown in box1002). The artisan will understand that where the monitored individualis sitting, majority of his weight will be borne by the wheelchair asopposed to the feet of the monitored individual. Graph 1050 likewiseindicates that when sitting C (i.e., at the time corresponding to “C” ingraph 1050), the pressure being applied by the wheelchair wheels (orother chair's legs) is significant whereas the pressure being applied bythe feet of the monitored individual is negligible.

Sit-to Stand activity D may be identified by the system 200 where themap 1000 indicates that the plantar pressure being applied (and as seenin box 1002) is comparable to the pressure being applied to the carpet100 by the wheels of wheelchairs (as seen in boxes 1004A, 1004B). Graph1050 illustrates that when the individual is standing up from a sittingposition (i.e., at the time corresponding to “D” in the graph 1050), thepressure being applied by the wheelchairs wheel is decreasing whereasthe plantar pressure increases correspondingly, which may allow thesystem 200 to determine the standing condition D.

FIG. 11 shows exemplary signal processing by system 200 of FIG. 2 todetermine breathing rate and heart rate from data of smart carpet 100 ofFIG. 1. First, the system 200 (e.g., monitoring computer 204) maycollect the raw data 1102 from the smart carpet 100. The raw data 1102may then be filtered, as shown in FIG. 11 for example, to yield filtereddata 1104 for breathing. The filtered breathing data 1104 may next becompared by the system 200 to reference breathing data 1106 (e.g.,expected breathing data), and the comparison may be used to estimate thebreathing rate of the monitored individual. As noted above, differentreference breathing data 1106 may be chosen when estimating thebreathing rate of different monitored individuals, or when estimatingthe breathing rate of individuals during certain activities (e.g.,walking, running, et cetera). If the evaluation shows the existence ofan abnormal condition (e.g., if the filtered breathing data 1104 doesnot correspond generally to the expected breathing data 1106 for a timeperiod), an alarm (e.g., alarm actuator 242) may be actuated to apprisemonitoring personnel of the emergency event.

In an example embodiment, the heart rate may be similarly evaluated.Specifically, the raw carpet data 1102 may be filtered to yield filtereddata for heart rate 1108. As shown, different cutoff frequencies may beused to filter the raw data for the breathing and heart rate estimation.The filtered data for heart rate 1108 may then be compared by the system200 to reference electrocardiogram data 1110 (e.g., expectedelectrocardiogram data for the monitored individual), and the comparisonmay be used to estimate the heart rate of the monitored individual. Thereference electrocardiogram data 1110 may be chosen to correspond to theexpected heart rate of the particular individual being monitored, andmay take into account whether the monitored individual is engaged in anactivity (e.g., is running).

In this way, thus, the system 200, via the smart carpet 100, may monitorthe healthcare of individuals continuously and non-obtrusively, and mayapprise remote monitoring personnel in real-time of emergencyconditions.

Changes may be made in the above methods and systems without departingfrom the scope hereof. It should be noted that the matter contained inthe above description or shown in the accompanying drawings should beinterpreted as illustrative and not in a limiting sense. The followingclaims are intended to cover all generic and specific features describedherein, as well as all statements of the scope of the present method andsystem, which, as a matter of language, might be said to falltherebetween. In particular, the following embodiments are specificallycontemplated, as well as any combinations of such embodiments that arecompatible with one another:

(A) A method of using a smart carpet having a plurality of pressuresensors arranged as a two-dimensional array, each pressure sensorincluding a block of anti-static foam situated between at least twoperpendicularly oriented yarns, including operably coupling to the smartcarpet a monitoring computer having a processor and non-transitorymemory having computer implemented instructions stored thereon;simultaneously scanning a first part of the smart carpet at a firstfrequency and a second part of the smart carpet at a second frequencygreater than the first frequency; receiving carpet data from the smartcarpet over a communication pathway; and processing the carpet datausing the computer implemented instructions to determine each of aphysiological activity and a physical activity.

(B) The method of using the smart carpet to monitor the health status ofthe individual denoted above as (A), including the step of generating analarm upon determining an emergency condition.

(C) Either of the methods of using the smart carpet to monitor thehealth status of the individual denoted above as (A) and (B), whereinthe alarm is communicated to remote monitoring personnel in real time.

(D) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(C), further including thestep of evaluating the carpet data to estimate a breathing rate of theindividual.

(E) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(D), wherein estimation ofthe breathing rate includes filtering the carpet data to obtain filteredbreathing data and comparing the filtered breathing data to a referencesignal.

(F) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(E), wherein theevaluation comprises using the carpet data to estimate a heart rate ofthe individual.

(G) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(F), including filteringthe carpet data to obtain filtered heart rate data and comparing thefiltered heart rate data to a reference heart rate signal, the referenceheart rate signal being disparate from the reference breathing signal.

(H) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(G), including the step ofdetermining a balance problem in the individual.

(I) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(H), wherein the balanceproblem is determined when the carpet data indicates at least one of anasymmetrical standing balance, an abnormal walking speed, and anirregular walking pattern.

(J) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(I), further comprisingprocessing the carpet data to categorize the physical activity as one ofa static activity and a dynamic activity.

(K) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(J), wherein based on theprocessing, the static activity is categorized as one of a sittingactivity, a standing activity, a lying activity, a sit-to-standactivity, and a stand-to-sit activity.

(L) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(K), further comprisingdetermining the sit-to-stand activity where at least one of: (a) plantarpressure on the second part increases rapidly; and (b) pressure appliedto the second part by an object decreases rapidly.

(M) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(L), wherein the objectapplying the pressure to the second part is a chair.

(N) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(M), further comprisingdetermining the sit-to-stand activity by comparing plantar pressure onthe second part with pressure applied by a chair.

(O) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(N), further comprisingdisplaying a map illustrating relative pressure being applied to eachpressure sensor constituting the second part.

(P) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(O), wherein each of thefirst part and the second part are adaptively selected based on thephysical activity.

(Q) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(P), further comprisingthe step of estimating a body weight of the individual during thestanding activity.

(R) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(Q), wherein each of theheart rate and the breathing rate is estimated during each of thesitting activity and the lying activity.

(S) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(R), wherein thecategorized dynamic activity includes at least one of walking, running,and falling.

(T) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(S), wherein the alarm isgenerated upon a determination that the individual has fallen and isunable to rise from the smart carpet for a time period.

(U) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(T), further comprisingprocessing the carpet data to measure the timing and distance of twoconsecutive steps to determine temporal and spatial parameters of gait.

(V) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(U), further comprisingprocessing the carpet data to detect an urgent event selected from thegroup including (a) a slow or stopped heart rate during an identifiedsitting event or an identified fall event, (b) a slow or stoppedrespiration rate during the identified sitting event or the identifiedfall event, and (c) an inability of the person to rise from the carpetafter the identified fall event; and sending an alert in response to thedetected urgent event.

(W) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(V), further comprisingprocessing the carpet data to identify a frailty status based upon atleast one of (i) a non-linear postural transition duration, (ii) anumber of steps required to complete a turn, (iii) a number of floppingand cautious sitting occurrences during a predefined period, (iv) adifference between heart rate during static sitting and static standing,(v) spatial and temporal parameters of gait, (vi) a number of stepsrequired to complete gait initiation, (vii) a duration of staticstanding and static sitting, and (viii) gait variability.

(X) Any of the methods of using the smart carpet to monitor the healthstatus of the individual denoted above as (A)-(W), further comprisingsituating the smart carpet on stairs to determine an up-the-stairsactivity.

(Y) A smart carpet system for monitoring a health status of anindividual, comprising a smart carpet having electronics and a pluralityof pressure sensing areas for sensing plantar pressure of theindividual; and a monitoring computer comprising a processor, and amemory communicatively coupled to the processor and storing machinereadable instructions; the instructions, when executed by the processor,capable of scanning a first part of the smart carpet at a firstfrequency and a second part of the smart carpet at a second frequencygreater than the first frequency, receiving plantar pressures from thesecond part, evaluating the plantar pressures to determine at least oneof a physical activity and a physiological activity, and communicatingto monitoring personnel an alarm upon determining an emergencycondition.

(Z) The smart carpet system for monitoring a health status of anindividual denoted at (Y), wherein the electronics include each of amultiplexer, a demultiplexer, and an analog to digital converter.

(AA) Either of the smart carpet systems for monitoring a health statusof an individual denoted at (Y)-(Z), wherein the monitoring computer iscoupled to the smart carpet via a communication pathway that extendsfrom the analog to digital converter.

(BB) Any of the smart carpet systems for monitoring a health status ofan individual denoted at (Y)-(AA), further comprising a remote server toreceive and store results of the evaluation.

(CC) A smart carpet system for monitoring a health status of anindividual, comprising a smart carpet having electronics and a pluralityof pressure sensing areas for sensing plantar pressure of theindividual; an activity-determining processor configured to implementmachine readable instructions to determine at least one of a physicalactivity and a physiological activity; an interface configured tocommunicate results of the determination to remote monitoring personnel;and an alarm generator configured to generate an alarm when the resultsindicate an emergency event.

What is claimed is:
 1. A health status monitoring system, comprising: asmart carpet having electronics and a grid of individually scannableresistance based pressure sensing nodes responsive to foot pressure ofan individual on the smart carpet; and a monitoring computer comprising:a processor; a memory communicatively coupled to the processor andstoring machine readable instructions which, when executed by theprocessor, are capable of: scanning fewer than all of the nodes in afirst area of the smart carpet where motion of the individual is notdetected at a first sampling frequency, and scanning all nodes in asecond area of the smart carpet where motion of the individual isdetected at a second sampling frequency greater than the first samplingfrequency to obtain carpet data; determining plantar pressures from thesecond part; and evaluating the plantar pressures in the carpet data todetermine physiological activity of the individual.
 2. The system ofclaim 1, the machine readable instructions further comprisinginstructions executable by the processor to communicate thephysiological activity to monitoring personnel.
 3. The system of claim1, the machine readable instructions further comprising instructionsexecutable by the processor to determine the physiological activity as afall of the individual on the smart carpet.
 4. The system of claim 1,wherein the monitoring computer is coupled to the smart carpet via acommunication pathway that extends from an analog to digital converter.5. The system of claim 1, the machine readable instructions furthercomprising instructions executable by the processor to scan the firstpart of the smart carpet at a resolution lower than the resolution ofscanning the second part at the second frequency.
 6. The system of claim1, the machine readable instructions further comprising instructionsexecutable by the processor to determine the physiological activity asheart rate of the individual.
 7. The system of claim 1, the machinereadable instructions further comprising instructions executable by theprocessor to determine the physiological activity as respiration rate ofthe individual.
 8. The system of claim 1, the machine readableinstructions further comprising instructions executable by the processorto: determine that the individual is standing and not moving; anddetermine a body weight of the individual based upon the plantarpressure.
 9. The system of claim 1, further comprising a remoteinterface configured to communicate the physiological activity to themonitoring personnel when located remotely from the smart carpet. 10.The system of claim 1, the smart carpet being substantially planar. 11.The system of claim 1, each of the pressure sensing nodes comprising ablock of anti-static foam positioned between at least twoperpendicularly oriented conductive yarns at a crossing point of the atleast two perpendicularly oriented conductive yarns.
 12. The system ofclaim 11, a resistance of the anti-static foam between the at least twoperpendicularly oriented conductive yarns being inversely proportionalto the plantar pressure applied to the node.
 13. The system of claim 11,the blocks of anti-static foam and the perpendicularly orientedconductive yarns being attached to an underside of a regular residentialcarpet.
 14. The system of claim 11, the blocks of anti-static foam andthe perpendicularly oriented conductive yarns covering an area the sizeof the regular carpet.
 15. The system of claim 11, the blocks ofanti-static foam and the perpendicularly oriented conductive yarns beingpositioned directly into a regular residential carpet.
 16. The system ofclaim 1, the grid of individually scannable resistance based pressuresensing nodes having a resolution of one centimeter.
 17. The system ofclaim 1, the fewer than all nodes of the first area being one in onehundred nodes.
 18. The system of claim 1, the smart carpet beingflexible for use on stairs.